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Metal-organic frameworks (MOFs) have recently emerged as a new type of nanoporous materials with tailorable structures and functions. Usually, MOFs have uniform pores smaller than 2 nm in size, limiting their practical applications in some cases. Although a few approaches have been adopted to prepare MOFs with larger pores, it is still challenging to synthesize hierarchical-pore MOFs (H-MOFs) with high structural controllability and good stability. Here we demonstrate a facile and versatile method, an in situ self-assembly template strategy for fabricating stable H-MOFs, in which multi-scale soluble and/or acid-sensitive metal-organic assembly (MOA) fragments form during the reactions between metal ions and organic ligands (to construct MOFs), and act as removable dynamic chemical templates. This general strategy was successfully used to prepare various H-MOFs that show rich porous properties and potential applications, such as in large molecule adsorption. Notably, the mesopore sizes of the H-MOFs can be tuned by varying the amount of templates. The synthesis of porous materials with hierarchically-sized pores is an attractive target for a variety of applications. Here, the authors employ in situ generated metal-organic assemblies as metal-organic framework templates which, upon removal, yield single mesoporous-microporous materials.

Determining the causal factors influencing college students’ willingness to consume digital music in China is crucial given the rapid integration of digital technology with the traditional music industry. Chengdu, as a rapidly developing city in China with a thriving youth culture and a significant presence of higher education institutions, provides an ideal setting to explore the factors influencing college students’ willingness to consume digital music. Using a mixed-method approach and a sample of 431 college students from various universities in Chengdu, this research examines the impact of perceived value, behavioral attitude, subjective norms, user participation, user stickiness, and psychological needs on digital music consumption intention. The empirical results show that these factors jointly affect consumption intention, with user stickiness and psychological needs serving as mediators. Specifically, perceived value, behavioral attitude, subjective norms, and user participation have a significant positive impact on consumption intention. These findings provide valuable insights for digital music platforms and the music industry to develop targeted marketing strategies and services tailored to the demands and behaviors of college students in Chengdu.

The high porosity and tunable chemical functionality of metal-organic frameworks (MOFs) make it a promising catalyst design platform. High-throughput screening of catalytic performance is feasible since the large MOF structure database is available. In this study, we report a machine learning model for high-throughput screening of MOF catalysts for the CO2 cycloaddition reaction. The descriptors for model training were judiciously chosen according to the reaction mechanism, which leads to high accuracy up to 97% for the 75% quantile of the training set as the classification criterion. The feature contribution was further evaluated with SHAP and PDP analysis to provide a certain physical understanding. 12,415 hypothetical MOF structures and 100 reported MOFs were evaluated under 100 °C and 1 bar within one day using the model, and 239 potentially efficient catalysts were discovered. Among them, MOF-76(Y) achieved the top performance experimentally among reported MOFs, in good agreement with the prediction. High-throughput screening of MOFs catalysts for CO2 cycloaddition has been accomplished in a relatively short period of time with a machine learning model. The combined high efficiency and accuracy of the model was attributed to the judicious choice of easily accessible descriptors based on the reaction mechanism. The experimental catalytic performance of MOF-76(Y) agrees well with the prediction results, as a top performing catalyst. [Display omitted] •High-throughput screening of MOF catalysts has been realized with an explainable machine learning model.•The judicious selection of descriptors, anchored in the reaction mechanism, enabled the model's accuracy and efficiency.•The high-throughput screening revealed 239 highly active catalysts from over 12,515 MOFs.•The MOF-76 (Y) material was identified as a top-performing catalyst, in good agreement with the prediction.

UiO-66 analogues are good candidates as stationary phase in HPLC because of their chemical/thermal stability, large surface area, and two cage structures. Here, two UiO-66 analogues, UiO-66-NH2 and UiO-67, were synthesized and used as stationary phase in HPLC to evaluate their performance in the separation of substituted benzenes (SBs) and polycyclic aromatic hydrocarbons (PAHs). The results showed that SBs could be well separated on UiO-66-NH2 column but not on UiO-67 column. Nonetheless, PAHs could be well separated on UiO-67 column. The separation mechanisms of SBs and PAHs on UiO-66 analogues may be involved in the pore size and functional group in the frameworks of UiO-66 analogues. Introduction of the-NH2 into UiO-66 significantly reduced its adsorption capacity for SB congeners, which resulted in less separation of SBs on UiO-66-NH2. As for the separation of PAHs on UiO-67 column, the π-π stacking effect was supposed to play a vital role.

Framework materials (FMs) have been extensively investigated with a plethora of literature documenting their unique properties and potential applications. Despite this, a comprehensive knowledge graph for this emerging field has not yet been constructed. In this study, by utilizing the natural language processing capabilities of large language models (LLMs), we have established a comprehensive knowledge graph (KG-FM). It covers synthesis, properties, applications, and other aspects of FMs including metal-organic frameworks (MOFs), covalent-organic frameworks (COFs), and hydrogen-bonded organic frameworks (HOFs). The knowledge graph was constructed through the analysis of over 100,000 articles, resulting in 2.53 million nodes and 4.01 million relationships. Subsequently, its application has been explored for enhancing data retrieval, mining, and the development of sophisticated question-answering systems. Especially when integrating the KGs with LLMs, resulted Qwen2-KG not only achieves a higher accuracy rate of 91.67% in question-answering than existing models but also provides precise information sources.

Silent hypoxia has emerged as a unique feature of coronavirus disease 2019 (COVID-19). In this study, we show that mucins are accumulated in the bronchoalveolar lavage fluid (BALF) of COVID-19 patients and are upregulated in the lungs of severe respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected mice and macaques. We find that induction of either interferon (IFN)-β or IFN-γ upon SARS-CoV-2 infection results in activation of aryl hydrocarbon receptor (AhR) signaling through an IDO-Kyn-dependent pathway, leading to transcriptional upregulation of the expression of mucins, both the secreted and membrane-bound, in alveolar epithelial cells. Consequently, accumulated alveolar mucus affects the blood-gas barrier, thus inducing hypoxia and diminishing lung capacity, which can be reversed by blocking AhR activity. These findings potentially explain the silent hypoxia formation in COVID-19 patients, and suggest a possible intervention strategy by targeting the AhR pathway.